Ten Challenges for Decision Neuroscience

Decision neuroscience research, as currently practiced, employs the methods of neuroscience to investigate concepts drawn from the social sciences. A typical study selects one or more variables from psychological or economic models, manipulates or measures choices within a simplified choice task, and then identifies neural correlates. Using this “neuroeconomic” approach, researchers have described brain systems whose functioning shapes key economic variables, most notably aspects of subjective value. Yet, the standard approach has fundamental limitations. Important aspects of the mechanisms of decision making – from the sources of variability in decision making to the very computations supported by decision-related regions – remain incompletely understood. Here, I outline 10 outstanding challenges for future research in decision neuroscience. While some will be readily addressed using current methods, others will require new conceptual frameworks. Accordingly, a new strain of decision neuroscience will marry methods from economics and cognitive science to concepts from neurobiology and cognitive neuroscience

Neural Substrates of Contingency Learning and Executive Control: Dissociating Physical, Valuative, and Behavioral Changes

Contingency learning is fundamental to cognition. Knowledge about environmental contingencies allows behavioral flexibility, as executive control processes accommodate the demands of novel or changing environments. Studies of experiential learning have focused on the relationship between actions and the values of associated outcomes. However, outcome values have often been confounded with the physical changes in the outcomes themselves. Here, we dissociated contingency learning into valuative and non-valuative forms, using a novel version of the two-alternative choice task, while measuring the neural effects of contingency changes using functional magnetic resonance imaging (fMRI). Changes in value-relevant contingencies evoked activation in the lateral prefrontal cortex (LPFC), posterior parietal cortex (PPC), and dorsomedial prefrontal cortex (DMPFC) consistent with prior results (e.g., reversal-learning paradigms). Changes in physical contingencies unrelated to value or to action produced similar activations within the LPFC, indicating that LPFC may engage in generalized contingency learning that is not specific to valuation. In contrast, contingency changes that required behavioral shifts evoked activation localized to the DMPFC, supplementary motor, and precentral cortices, suggesting that these regions play more specific roles within the executive control of behavior

Prosocial Reward Learning in Children and Adolescents

Adolescence is a period of increased sensitivity to social contexts. To evaluate how social context sensitivity changes over development—and influences reward learning—we investigated how children and adolescents perceive and integrate rewards for oneself and others during a dynamic risky decision-making task. Children and adolescents (N = 75, 8–16 years) performed the Social Gambling Task (SGT, Kwak et al., 2014) and completed a set of questionnaires measuring other-regarding behavior. In the SGT, participants choose amongst four card decks that have different payout structures for oneself and for a charity. We examined patterns of choices, overall decision strategies, and how reward outcomes led to trial-by-trial adjustments in behavior, as estimated using a reinforcement-learning model. Performance of children and adolescents was compared to data from a previously collected sample of adults (N = 102) performing the identical task. We found that that children/adolescents were not only more sensitive to rewards directed to the charity than self but also showed greater prosocial tendencies on independent measures of other-regarding behavior. Children and adolescents also showed less use of a strategy that prioritizes rewards for self at the expense of rewards for others. These results support the conclusion that, compared to adults, children and adolescents show greater sensitivity to outcomes for others when making decisions and learning about potential rewards

Healthful choices depend on the latency and rate of information accumulation

The drift diffusion model provides a parsimonious explanation of decisions across neurobiological, psychological and behavioural levels of analysis. Although most drift diffusion model implementations assume that only a single value guides decisions, choices often involve multiple attributes that could make separable contributions to choice. Here we fit incentive-compatible dietary choices to a multi-attribute, time-dependent drift diffusion model, in which taste and health could differentially influence the evidence accumulation process. We find that these attributes shaped both the relative value signal and the latency of evidence accumulation in a manner consistent with participants’ idiosyncratic preferences. Moreover, by using a dietary prime, we showed how a healthy choice intervention alters multi-attribute, time-dependent drift diffusion model parameters that in turn predict prime-dependent choices. Our results reveal that different decision attributes make separable contributions to the strength and timing of evidence accumulation, providing new insights into the construction of interventions to alter the processes of choice

Neurocognitive Development of Risk Aversion from Early Childhood to Adulthood

Human adults tend to avoid risk. In behavioral economic studies, risk aversion is manifest as a preference for sure gains over uncertain gains. However, children tend to be less averse to risk than adults. Given that many of the brain regions supporting decision-making under risk do not reach maturity until late adolescence or beyond it is possible that mature risk-averse behavior may emerge from the development of decision-making circuitry. To explore this hypothesis, we tested 5- to 8-year-old children, 14- to 16-year-old adolescents, and young adults in a risky-decision task during functional magnetic resonance imaging (fMRI) data acquisition. To our knowledge, this is the youngest sample of children in an fMRI decision-making task. We found a number of decision-related brain regions to increase in activation with age during decision-making, including areas associated with contextual memory retrieval and the incorporation of prior outcomes into the current decision-making strategy, e.g., insula, hippocampus, and amygdala. Further, children who were more risk-averse showed increased activation during decision-making in ventromedial prefrontal cortex and ventral striatum. Our findings indicate that the emergence of adult levels of risk aversion co-occurs with the recruitment of regions supporting decision-making under risk, including the integration of prior outcomes into current decision-making behavior. This pattern of results suggests that individual differences in the development of risk aversion may reflect differences in the maturation of these neural processes

Eyes on the account size:Interactions between attention and budget in consumer choice*

The context surrounding a consumer decision, such as one's overall budget available for pur-chases, can exert a strong effect on the subjective value of a product. Across three eye-tracking studies, we explore the attentional processes through which budget size influences consumers' purchasing behavior. Higher budgets increased and sped up purchasing even when items were affordable at all budget sizes. Moreover, attention interacted with budget size to promote pur-chasing at higher budgets. Finally, individual differences in the magnitude of the budget effect related to attentional patterns: those whose decisions depended more on budget exhibited more budget-price transitions and less variability in search patterns compared to those whose decisions were less dependent on budget. These findings indicate that attention moderates the effect of budgets on purchasing decisions, allowing low budgets to serve as self-control devices and large budgets to generate impulse purchases

Peer presence increases the prosocial behavior of adolescents by speeding the evaluation of outcomes for others

Peer presence can elicit maladaptive adolescent decision-making, potentially by increasing sensitivity to the rewards one receives. It remains unknown whether peer presence also increases adolescents’ sensitivity to others’ outcomes, which could have an adaptive effect in contexts allowing pro-social behaviors. Here, we combine social utility modeling and real-time decision process modeling to characterize how peer presence alters adolescents’ processing of self and other outcomes. We found that adolescents behaved selfishly when privately allocating monetary rewards for themselves and a peer in an incentive-compatible task. In peer presence, however, adolescents became more altruistic. Real-time decision process estimates collected using computer mouse tracking showed that altruistic behavior was associated with relatively earlier influence of peer-outcomes relative to self-outcomes, and that peer presence sped the influence of peer-outcomes without altering the time at which self-outcomes began to influence the decision process. Our results indicate a mechanism through which peer presence prompts greater prosocial behavior by altering how adolescents process prosocial outcomes

What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits

Nucleus Accumbens Mediates Relative Motivation for Rewards in the Absence of Choice

To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation